NotesAnkify – Convert PDF Notes to Anki Flashcards Automatically(No AI)

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Inspired by Steph Ango's "File over app" philosophy [ https://stephanango.com/file-over-app ], I built NotesAnkify to address a frustration with modern note-taking apps: their ephemeral, locked-in flashcard features. While apps like GoodNotes and Notability keep building and rebuilding flashcard features that come and go with updates, Anki has remained a reliable, open-source spaced repetition system for over 16 years. Its plain text deck format and open API make it more aligned with the "file over app" ethos - your flashcards remain accessible and portable. NotesAnkify bridges this gap. It lets you export flashcards in your notes from any note-taking app (GoodNotes, Notability, OneNote, Nebo, etc.) and converts them into Anki flashcards that you own and can take anywhere. What it does: - Automatically processes PDFs from any note-taking app (GoodNotes, Notability, OneNote, Nebo etc.) - Detects flashcards using either QUESTION/ANSWER markers or dimensions. - Maintains proper deck hierarchy based on folder structure. - Prevents duplicates using SHA-256 content hashing. - Works on MacOS, Windows, and Linux Tech stack: - Go for the backend processing. - Fyne framework ( https://fyne.io/ ) for GUI. - Integration with AnkiConnect API. - Ginkgo for testing framework. The entire project is open source [ https://github.com/kpauljoseph/notesankify ] Documentation: [ https://notesankify.com/docs ]

Visit product View on Hacker NewsFeb 16, 2025

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Hacker NewsStrong engagement from HN community · Strong signals: open source, io · Missing: https docs, excited, just released

79%79% predicted probability of success on Hacker News, based on ML models trained on real launch data.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

70%70% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

29%29% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

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BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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